Transfer Learning: Techniques and Applications
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 November 2025 | Viewed by 20
Special Issue Editors
Interests: machine learning and deep learning; industrial artificial intelligence; transfer learning and lifelong learning; deep reinforcement learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Transfer learning has become essential in advancing artificial intelligence and deep learning, enabling the effective reuse of knowledge across domains, tasks, and data distributions. It plays a critical role in overcoming challenges such as data scarcity, domain shift, and generalization to unseen environments, making it vital in AI applications.
This Special Issue aims to compile recent advances in transfer learning, including novel methods, theoretical foundations, and practical applications. Topics of interest include, but are not limited to, the following:
- Novel methods and theoretical frameworks for transfer learning and domain adaptation.
- Novel pre-training strategies (e.g., self-supervised, supervised, domain-specific) and their impact on downstream tasks.
- Novel methods for cross-domain, cross-task and cross-modal transfer learning.
- Techniques for dealing with domain shift, catastrophic forgetting, and negative transfer.
- Transfer learning across different data modalities (e.g., vision, text, speech, graphs, time series) or structures.
- Applications of transfer learning to solve real-world challenges (e.g., simulation to reality, medical image analysis, robotics, autonomous systems, computer vision, engineering and Industry 4.0).
- Empirical investigations and benchmarks comparing different transfer learning approaches.
- Investigations into the robustness, fairness, and limitations of transfer learning methods.
We hope to provide a collection of high-quality papers that reflect the current state of the art and emerging research directions in transfer learning.
We look forward to receiving your contributions.
Dr. Hasan Tercan
Dr. Yang Li
Guest Editors
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- transfer learning
- domain adaptation
- deep learning
- artificial intelligence
- pre-training
- knowledge transfer
- machine learning
- sim-to-real transfer
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